Restoring Health to Health Reform: Integrating Medicine and Public Health to Advance the Population's Well-Being
Bibliographic record
Abstract
The Patient Protection and Affordable Care Act is a major achievement in improving access to health care services. However, evidence indicates that the nation could achieve greater improvements in health outcomes, at a lower cost, by shifting its focus to public health. By focusing nearly exclusively on health care, policy makers have chronically starved public health of adequate and stable funding and political support. The lack of support for public health is exacerbated by the fact that health care and public health are generally conceptualized, organized, and funded as two separate systems. In order to maximize gains in health status and to spend scarce health resources most effectively, health care and public health should be treated as two interactive parts of a single, unified health system. The core purpose of health reform ought to be the improvement of the population’s health. We propose five criteria that would significantly advance this goal: prevention and wellness, human resources, a strong and sustainable health infrastructure, robust performance measurement, and reduction of health disparities. Although the Patient Protection and Affordable Care Act includes provisions addressing these criteria, population health is not a central focus of the reform. In order to guide health reform implementation and to inform future health reform efforts, we offer three major policy reforms: changing the environment to incentivize healthy behavioral choices, strengthening the public health infrastructure at the state and local levels, and developing a health-in-all policies strategy that would engage multiple agencies in improving health incomes. Adopting these reforms would facilitate integration and dramatically improve the population’s health, particularly when compared to the health gains likely to be realized from a continued focus on access to health care services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.053 |
| Scholarly communication | 0.025 | 0.030 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.021 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".